class_docstr: An implementation of `"NEU" <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_
  from the IJCAI 17 paper "Fast Network Embedding Enhancement via High Order Proximity Approximation".
  The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive
  meta learning algorithm.
  
  Args:
      L1 (float): Weight of lower order proximities. Defauls is 0.5
      L2 (float): Weight of higher order proximities. Default is 0.25.
      T (int): Number of iterations. Default is 1.
      seed (int): Random seed value. Default is 42.
docstr: Getting the node embedding.
  
  Return types:
      * **embedding** *(Numpy array)* - The embedding of nodes.
docstr_tok: ["Getting","the","node","embedding",".","Return","types",":","*","*","*","embedding","*","*","*","(","Numpy","array",")","*","-","The","embedding","of","nodes","."]
code: def get_embedding(self) -> np.array:
      r"""Getting the node embedding.
  
      Return types:
          * **embedding** *(Numpy array)* - The embedding of nodes.
      """
      return self._embedding
  
file_path: karateclub/karateclub/node_embedding/meta/neu.py
imports: import numpy
  import networkx
  from sklearn.preprocessing import normalize
  from karateclub.estimator import Estimator
label_desc: Use image node_id 5 for calling the NEU obj's underlying member method code with example usage: obj.get_embedding() and returns: self